The invention relates generally to the field of image processing, and in particular to the field of image assessment and understanding.
Image assessment and understanding deal with problems that are easily solved by human beings given their intellectual faculties but are extremely difficult to solve by fully automated computer systems. Image understanding problems that are considered important in photographic applications include main subject detection, scene classification, sky and grass detection, people detection, automatic detection of orientation, etc. In a variety of applications that deal with a group of pictures, it is important to rank the images in terms of a logical order, so that they can be processed or treated according to their order. A photographic application of interest is automatic albuming, where a group of digital images are automatically organized into digital photo albums. This involves clustering the images into separate events and then laying out each event in some logical order, if possible. This order implies at least some attention to the relative content of the images, i.e., based on the belief that some images would likely be preferred over others.
Typically, digital imaging systems that store groups of images in a fixed storage space apply the same level of compression to all images in the group. This may be the situation for images stored in digital cameras, portable disks, etc. However, this approach does not take into consideration differences in emphasis or appeal between images. It is often desirable to maintain the visual quality of images that are appealing, while it is tolerable to degrade the visual quality of images that are not appealing. Therefore, it is desirable to obtain a digital system that ranks images in terms of their relative appeal and uses the results of this ranking to vary the amount of compression applied to each image, so that the higher quality is maintained for higher appeal images.
Due to the nature of the image assessment problem, i.e., that an automated system is expected to generate results that are representative of high-level cognitive human (understanding) processes, the design of an assessment system is a challenging task. Effort has been devoted to evaluating text and graphical data for its psychological effect, with the aim of creating or editing a document for a particular visual impression (see, e.g., U.S. Pat. Nos. 5,875,265 and 5,424,945). In the ""265 patent, a system analyzes an image, in some case with the aid of an operator, to determine correspondence of visual features to sensitive language that is displayed for use by the operator. The difficulty in this system is that the visual features are primarily based on low level features, i.e., color and texture, that are not necessarily related to image content, and a language description is difficult is to use for relative ranking of images. The ""945 patent discloses a system for evaluating the psychological effect of text and graphics in a document. The drawback with the ""945 patent is that it evaluates the overall visual impression of the document, without regard to its specific content, which reduces its usefulness for developing relative ranking. Besides their complexity and orientation toward discernment of a psychological effect, these systems focus on the analysis and creation of a perceptual impression rather than on the assessment and utilization of an existing image.
The present invention is directed to overcoming one or more of the problems set forth above. In one embodiment, the amount of compression for an image in the group is controlled using a quality factor whose value is related to image emphasis/appeal of the image that is compressed. In another embodiment, compression is controlled based on visual quality, and in another embodiment compression is controlled based on output file size. In all cases, the image parameters that determine the level compression are a function of image emphasis/appeal.
The determination of image emphasis or appeal, i.e., the degree of importance, interest or attractiveness of an image is based on an assessment of the image with respect to certain features, wherein one or more quantities are computed that are related to one or more features in each digital image, including one or more features pertaining to the content of the individual digital image. The quantities are processed with a reasoning algorithm that is trained on the opinions of one or more human observers, and an output is obtained from the reasoning algorithm that assesses each image. In a dependent aspect of the invention, the features pertaining to the content of the digital image include at least one of people-related features and subject-related features. Moreover, additional quantities may be computed that relate to one or more objective measures of the digital image, such as colorfulness or sharpness. The results of the reasoning algorithm are processed to rank order the quality of each image in the set of images. The amount of compression applied to each digital image are varied based on the degree of importance, interest or attractiveness of the image, determined as by itself or as related to the group of digital images.
The invention automatically varies the compression of images by ranking the images within clusters based upon image emphasis. The ranking process includes computes one or more xe2x80x9cquantitiesxe2x80x9d related to one or more features in each image and the content of the images. The invention processes the quantities with a reasoning algorithm that is trained based on opinions of one or more human observers and applies the quantities to the images to produce the ranking. The invention variably compresses the images depending upon the ranking such that images having a low ranking are compressed more than images having a high ranking. The features analyzed can include people-related features and subject-related features. The objective features can include colorfulness, sharpness, representative quality in terms of color content, and uniqueness of picture aspect format. The reasoning algorithm is trained from ground truth studies of candidate images and is a Bayesian network. The content of the images is controlled using a quality factor whose value is related to image emphasis/appeal and is also controlled based on output file size whose value is related to image emphasis/appeal. The content of images is further controlled using the visual quality of an output image.
One advantage of the invention lies in its ability to perform an assessment of one or more images without human intervention. In a variety of applications that deal with a group of pictures, such as compression of groups of images, such an algorithmic assessment enables the automatic ranking of images, so that they can be more efficiently compressed according to their relative importance.
These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings.